| # Intensity normalization in nnU-Net |
|
|
| The type of intensity normalization applied in nnU-Net can be controlled via the `channel_names` (former `modalities`) |
| entry in the dataset.json. Just like the old nnU-Net, per-channel z-scoring as well as dataset-wide z-scoring based on |
| foreground intensities are supported. However, there have been a few additions as well. |
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|
| Reminder: The `channel_names` entry typically looks like this: |
|
|
| "channel_names": { |
| "0": "T2", |
| "1": "ADC" |
| }, |
| |
| It has as many entries as there are input channels for the given dataset. |
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|
| To tell you a secret, nnU-Net does not really care what your channels are called. We just use this to determine what normalization |
| scheme will be used for the given dataset. nnU-Net requires you to specify a normalization strategy for each of your input channels! |
| If you enter a channel name that is not in the following list, the default (`zscore`) will be used. |
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|
| Here is a list of currently available normalization schemes: |
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|
| - `CT`: Perform CT normalization. Specifically, collect intensity values from the foreground classes (all but the |
| background and ignore) from all training cases, compute the mean, standard deviation as well as the 0.5 and |
| 99.5 percentile of the values. Then clip to the percentiles, followed by subtraction of the mean and division with the |
| standard deviation. The normalization that is applied is the same for each training case (for this input channel). |
| The values used by nnU-Net for normalization are stored in the `foreground_intensity_properties_per_channel` entry in the |
| corresponding plans file. This normalization is suitable for modalities presenting physical quantities such as CT |
| images and ADC maps. |
| - `noNorm` : do not perform any normalization at all |
| - `rescale_to_0_1`: rescale the intensities to [0, 1] |
| - `rgb_to_0_1`: assumes uint8 inputs. Divides by 255 to rescale uint8 to [0, 1] |
| - `zscore`/anything else: perform z-scoring (subtract mean and standard deviation) separately for each train case |
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|
| **Important:** The nnU-Net default is to perform 'CT' normalization for CT images and 'zscore' for everything else! If |
| you deviate from that path, make sure to benchmark whether that actually improves results! |
|
|
| # How to implement custom normalization strategies? |
| - Head over to nnunetv2/preprocessing/normalization |
| - implement a new image normalization class by deriving from ImageNormalization |
| - register it in nnunetv2/preprocessing/normalization/map_channel_name_to_normalization.py:channel_name_to_normalization_mapping. |
| This is where you specify a channel name that should be associated with it |
| - use it by specifying the correct channel_name |
| |
| Normalization can only be applied to one channel at a time. There is currently no way of implementing a normalization scheme |
| that gets multiple channels as input to be used jointly! |
